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Bloomberg：Technology·· 4 小时前AI 评分47

Sequoia、Nvidia 等投资 Mecka AI 的机器人技术

Sequoia, Nvidia Back Mecka AI’s Robotics Push

AI 导读

Mecka AI 完成 6000 万美元 B 轮融资，由 Sequoia Capital 领投，Nvidia、微软 M12 和高通参投，用于构建帮助机器人在物理世界中理解和操作的技术。CEO Josh Gao 表示，机器人的核心挑战在于数据，Mecka 正把物理交互中的压力、力度等信息数字化，并强调合成数据的作用。

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Bloomberg Tech • Oct 7th, 2026

Sequoia, Nvidia Back Mecka AI’s Robotics Push

Mecka AI has raised $60 million in Series B funding led by Sequoia Capital, with backing from Nvidia, Microsoft’s M12 and Qualcomm, as it builds technology to help robots understand and operate in the physical world. CEO and cofounder Josh Gao discusses the enormous data challenge facing robotics, how Mecka captures everything from physical interactions to force and pressure, and why scaling physical AI will require major investments across sensors, infrastructure and real-world data collection. He joins Ed Ludlow on "Bloomberg Tech."

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0:00

You know, we do a lot on the show about world models that underpin physical eye

0:05

deployments, but the message is doing something slightly different.

0:08

Thanks so much for having me, Ed. Yeah, I think a big part of this is that

0:12

robotics is entering this deep learning era.

0:15

A big challenge for a lot of traditional robotics has been they weren't able to

0:18

generalize. The world is incredibly chaotic in the

0:22

amount of noise life changes, even the dexterity of a lot of motions that we

0:27

have to go through. You can imagine just understanding

0:30

physics is a very challenging problem. Yes.

0:32

And so for us, when we think about models, ultimately it comes down to

0:35

three things in this new deep learning area for us.

0:38

And that is one we have to have compute algorithms and data.

0:43

And so some of the work that we do is how can we digitize a lot of the

0:47

physical world. There is incredible detail in the world

0:51

about how much pressure it takes to interact with objects.

0:55

What happens if I drop a cup and it shatters on the floor?

0:58

How do I know where the pieces are going to be?

1:00

This imagination is really the data that we're trying to digitize and also the

1:05

how that data originates. Right.

1:06

So, you know, I think a case study I always think about is me watching

1:11

engineers control a robotic arm repetitively to generate data, or moving

1:16

an object from point A to point B, but there is now a huge role for synthetic

1:20

data. Um, getting lots of real world data is

1:23

hard, you know. How are you?

1:25

How are you taking that on? I think a great example is if you look

1:28

at AlphaGo, for example, okay, AlphaGo had behavior cloning with millions of

1:34

different go games played over time. And then ultimately through the rules of

1:37

the game, we were able to have self play.

1:40

Mhm. And the big difference here is that just

1:43

the rules of the game of go is the entire world in our situation.

1:48

The entire physical world can't be digitized right into rules right away.

1:53

And so the gap for us to be able to create what is a perfect world model or

1:57

perfect simulator for us to have synthetic data is a long ways away, and

2:01

hopefully we can fill that gap across many different modalities and domains.

2:05

Uh, how does Macca's business run? Like, What is it that you sell or what

2:09

is your product? Uh, there's ultimately three sacks in

2:12

robotics. It's.

2:13

Can we handle the hardware, the software and the operations really well.

2:17

So the first piece of the business is ultimately, does the hardware exist to

2:21

sensor rise this information in the world.

2:24

And so a great example actually is force data.

2:27

Typically when I'm grabbing a cup, when I'm, uh, opening a door handle, when I'm

2:31

twisting a bottle, how many Newtons does it take to apply the pressure?

2:36

Uh, and so our work is first, can we create those sensors?

2:39

If they don't exist? We manufacture, we design, we produce

2:42

them at scale. And the second is ultimately operations.

2:46

We distribute and run logistics internationally so that we can go into

2:50

different scenarios, all the edge cases, different types of businesses.

2:54

Uh, an excellent example in this case is, uh, we've also had Tony shoe backing

2:58

us. The door is probably, probably, probably

3:01

the best, uh, operator or top operator in the world at this point.

3:06

And so we've had to go through this muscle building of what does it take to

3:09

actually get the physical, uh, intelligence into the world, ultimately.

3:15

Before we started the segment with you said to me during the commercial break,

3:19

we are just getting started. Um, but you've raised significant amount

3:23

of capital, you know, what are the priorities for you?

3:25

Where you need to hire people and what it is you're going to do to scale?

3:31

I think a big piece of even thinking about the scale that we need to be, uh,

3:35

you can look at Tesla in terms of spending a decade to solve autonomous

3:39

driving. They solve for two degrees of freedom of

3:42

acceleration and then direction and robotics.

3:45

We have many dozens of degrees of freedom, even in our hands.

3:48

Every joint movement, every body movement.

3:51

And that scale of data is quite exponential.

3:53

So a lot of this funding and what we're trying to do is we're trying to lay the

3:56

infrastructure to make this possible in many regions, even to support this data,

4:01

we have to set up hundreds of gigabytes of upload bandwidth that doesn't exist

4:05

before. And so set up the infrastructure scale

4:08

in terms of hiring, attracting top tier robotics, talent, infrastructure,

4:12

operations, hardware, uh, and ultimately be able to scale up.

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